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DOE OSTI2013

THE Be/X-RAY BINARY SWIFT J1626.6-5156 AS A VARIABLE CYCLOTRON LINE SOURCE

Swift J1626.6-5156 is a Be/X-ray binary that was in outburst from 2005 December until 2008 November. We have examined Rossi X-ray Timing Explorer/Proportional Counter Array (PCA) and High Energy X-ray Timing Explorer spectra of three long observations of this source taken early in its outburst, when the PCA 2-20 keV count rate was >70 counts s{sup -1} PCU{sup -1}, as well as several combined observations from different stages of the outburst. The spectra are best fit with an absorbed cutoff power law with a {approx}6.4 keV iron emission line and a Gaussian optical depth absorption line at {approx}10 keV. We present strong evidence that this absorption-like feature is a cyclotron resonance scattering feature, making Swift J1626.6-5156 a new candidate cyclotron line source. The redshifted energy of {approx}10 keV implies a magnetic field strength of {approx}8.6(1 + z) Multiplication-Sign 10{sup 11} G in the region of the accretion column close to the magnetic poles where the cyclotron line is produced. Analysis of phase-averaged spectra spanning the duration of the outburst suggests a possible positive correlation between the fundamental cyclotron energy and source luminosity. Phase-resolved spectroscopy from a long observation reveals a variable cyclotron line energy, with phase dependence similar to a variety of other pulsars, as well as the first harmonic of the fundamental cyclotron line.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

ON THE THREE-DIMENSIONAL STRUCTURE OF THE MASS, METALLICITY, AND STAR FORMATION RATE SPACE FOR STAR-FORMING GALAXIES

We demonstrate that the space formed by the star formation rate (SFR), gas-phase metallicity (Z), and stellar mass (M {sub *}) can be reduced to a plane, as first proposed by Lara-Lopez et al. We study three different approaches to find the best representation of this 3D space, using a principal component analysis (PCA), a regression fit, and binning of the data. The PCA shows that this 3D space can be adequately represented in only two dimensions, i.e., a plane. We find that the plane that minimizes the {chi}{sup 2} for all variables, and hence provides the best representation of the data, corresponds to a regression fit to the stellar mass as a function of SFR and Z, M {sub *}= f(Z, SFR). We find that the distribution resulting from the median values in bins for our data gives the highest {chi}{sup 2}. We also show that the empirical calibrations to the oxygen abundance used to derive the Fundamental Metallicity Relation have important limitations, which contribute to the apparent inconsistencies. The main problem is that these empirical calibrations do not consider the ionization degree of the gas. Furthermore, the use of the N2 index to estimate oxygen abundances cannot be applied for 12 + log(O/H) {approx}> 8.8 because of the saturation of the [N II] {lambda}6584 line in the high-metallicity regime. Finally, we provide an update of the Fundamental Plane derived by Lara-Lopez et al.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of machine learning

Abstract A computational approach by an implementation of the principle component analysis (PCA) with K -means and Gaussian mixture (GM) clustering methods from machine learning algorithms to identify structural and dynamical heterogeneities of supercooled liquids is developed. In this method, a collection of the average weighted coordination numbers ( W C N s ‾ ) of particles calculated from particles’ positions are used as an order parameter to build a low-dimensional representation of feature (structural) space for K -means clustering to sort the particles in the system into few meso-states using PCA. Nano-domains or aggregated clusters are also formed in configurational (real) space from a direct mapping using associated meso-states’ particle identities with some misclassified interfacial particles. These classification uncertainties can be improved by a co-learning strategy which utilizes the probabilistic GM clustering and the information transfer between the structural space and configurational space iteratively until convergence. A final classification of meso-states in structural space and domains in configurational space are stable over long times and measured to have dynamical heterogeneities. Armed with such a classification protocol, various studies over the thermodynamic and dynamical properties of these domains indicate that the observed heterogeneity is the result of liquid–liquid phase separation after quenching to a supercooled state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2016

Cosmic reionization study: principle component analysis after Planck

The study of reionization history plays an important role in understanding the evolution of our universe. It is commonly believed that the intergalactic medium (IGM) in our universe are fully ionized today, however the reionizing process remains to be mysterious. A simple instantaneous reionization process is usually adopted in modern cosmology without direct observational evidence. However, the history of ionization fraction, x{sub e}(z) will influence CMB observables and constraints on optical depth τ. With the mocked future data sets based on featured reionization model, we find the bias on τ introduced by instantaneous model can not be neglected. In this paper, we study the cosmic reionization history in a model independent way, the so called principle component analysis (PCA) method, and reconstruct x{sub e} (z) at different redshift z with the data sets of Planck, WMAP 9 years temperature and polarization power spectra, combining with the baryon acoustic oscillation (BAO) from galaxy survey and type Ia supernovae (SN) Union 2.1 sample respectively. The results show that reconstructed x{sub e}(z) is consistent with instantaneous behavior, however, there exists slight deviation from this behavior at some epoch. With PCA method, after abandoning the noisy modes, we get stronger constraints, and the hints for featured x{sub e}(z) evolution could become a little more obvious.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

The influence of physical and algorithmic factors on simulated far-field waveforms and source–time functions of underground explosions using unsupervised machine learning

SUMMARY Characterizing explosion sources and differentiating between earthquake and underground explosions using distributed seismic networks becomes non-trivial when explosions are detonated in cavities or heterogeneous ground material. Moreover, there is little understanding of how changes in subsurface physical properties affect the far-field waveforms we record and use to infer information about the source. Simulations of underground explosions and the resultant ground motions can be a powerful tool to systematically explore how different subsurface properties affect far-field waveform features, but there are added variables that arise from how we choose to model the explosions that can confound interpretation. To assess how both subsurface properties and algorithmic choices affect the seismic wavefield and the estimated source functions, we ran a series of 2-D axisymmetric non-linear numerical explosion experiments and wave propagation simulations that explore a wide array of parameters. We then inverted the synthetic far-field waveform data using a linear inversion scheme to estimate source–time functions (STFs) for each simulation case. We applied principal component analysis (PCA), an unsupervised machine learning method, to both the far-field waveforms and STFs to identify the most important factors that control variance in the waveform data and differences between cases. For the far-field waveforms, the largest variance occurs in the shallower radial receiver channels in the 0–50 Hz frequency band. For the STFs, both peak amplitude and rise times across different frequencies contribute to the variance. We find that the ground equation of state (i.e. lithology and rheology) and the explosion emplacement conditions (i.e. tamped versus cavity) have the greatest effect on the variance of the far-field waveforms and STFs, with the ground yield strength and fracture pressure being secondary factors. Differences in the PCA results between the far-field waveforms and STFs could possibly be due to near-field non-linearities of the source that are not accounted for in the estimation of STFs and could be associated with yield strength, fracture pressure, cavity radius and cavity shape parameters. Other algorithmic parameters are found to be less important and cause less variance in both the far-field waveforms and STFs, meaning algorithmic choices in how we model explosions are less important, which is encouraging for the further use of explosion simulations to study how physical Earth properties affect seismic waveform features and estimated STFs.

58 GEOSCIENCES↗

DOE OSTI2020

Reionization history constraints from neural network based predictions of high-redshift quasar continua

ABSTRACT Observations of the early Universe suggest that reionization was complete by z ∼ 6, however, the exact history of this process is still unknown. One method for measuring the evolution of the neutral fraction throughout this epoch is via observing the Lyα damping wings of high-redshift quasars. In order to constrain the neutral fraction from quasar observations, one needs an accurate model of the quasar spectrum around Lyα, after the spectrum has been processed by its host galaxy but before it is altered by absorption and damping in the intervening intergalactic medium (IGM). In this paper, we present a novel machine learning approach, using artificial neural networks, to reconstruct quasar continua around Lyα. Our Quasar Spectra from Artificial Neural Network based predictive Regression Algorithm(QSANNdRA) improves the error in this reconstruction compared to the state-of-the-art principal component analysis (PCA) based model in the literature by 14.2 per cent on average, and provides an improvement of 6.1 per cent on average when compared to an extension thereof. In comparison with the extended PCA model, QSANNdRA further achieves an improvement of 22.1 per cent and 16.8 per cent when evaluated on low-redshift quasars most similar to the two high-redshift quasars under consideration, ULAS J1120+0641 at z = 7.0851 and ULAS J1342+0928 at z = 7.5413, respectively. Using our more accurate reconstructions of these two z > 7 quasars, we estimate the neutral fraction of the IGM using a homogeneous reionization model and find $\bar{x}_\mathrm{H\, \small{I}} = 0.25^{+0.05}_{-0.05}$ at z = 7.0851 and $\bar{x}_\mathrm{H\, \small{I}} = 0.60^{+0.11}_{-0.11}$ at z = 7.5413. Our results are consistent with the literature and favour a rapid end to reionization.

Ďurovčíková, Dominika↗

DOE OSTI2021

A comparative study of the physical properties for a representative sample of Narrow and Broad-line Seyfert galaxies

ABSTRACT We present a comparative study of the physical properties of a homogeneous sample of 144 Narrow line Seyfert 1 (NLSy1) and 117 Broad-line Seyfert 1 (BLSy1) galaxies. These two samples are in a similar luminosity and redshift range and have optical spectra available in the 16th data release of Sloan Digital Sky Survey (SDSS-DR16) and X-ray spectra in either XMM-NEWTON or ROSAT. Direct correlation analysis and a principal component analysis (PCA) have been performed using ten observational and physical parameters obtained by fitting the optical spectra and the soft X-ray photon indices as another parameter. We confirm that the established correlations for the general quasar population hold for both types of galaxies in this sample despite significant differences in the physical properties. We characterize the sample also using the line shape parameters, namely the asymmetry and kurtosis indices. We find that the fraction of NLSy1 galaxies showing outflow signatures, characterized by blue asymmetries, is higher by a factor of about 3 compared to the corresponding fraction in BLSy1 galaxies. The presence of high iron content in the broad-line region of NLSy1 galaxies in conjunction with higher Eddington ratios can be the possible reason behind this phenomenon. We also explore the possibility of using asymmetry in the emission lines as a tracer of outflows in the inner regions of Active Galactic Nuclei. The PCA results point to the NLSy1 and BLSy1 galaxies occupying different parameter spaces, which challenges the notion that NLSy1 galaxies are a subclass of BLSy1 galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

What drives the variance of galaxy spectra?

We present a study aimed at understanding the physical phenomena underlying the formation and evolution of galaxies following a data-driven analysis of spectroscopic data based on the variance in a carefully selected sample. We apply principal component analysis (PCA) independently to three subsets of continuum-subtracted optical spectra, segregated into their nebular emission activity as quiescent, star-forming, and active galactic nuclei (AGNs). We emphasize that the variance of the input data in this work only relates to the absorption lines in the photospheres of the stellar populations. The sample is taken from the Sloan Digital Sky Survey (SDSS) in the stellar velocity dispersion range 100–150 km s −1 , to minimize the ‘blurring’ effect of the stellar motion. We restrict the analysis to the first three principal components (PCs) and find that PCA segregates the three types with the highest variance mapping SSP-equivalent age, along with an inextricable degeneracy with metallicity, even when all three PCs are included. Spectral fitting shows that stellar age dominates PC1, whereas PC2 and PC3 have a mixed dependence of age and metallicity. The trends support – independently of any model fitting – the hypothesis of an evolutionary sequence from star formation to AGN to quiescence. As a further test of the consistency of the analysis, we apply the same methodology in different spectral windows, finding similar trends, but the variance is maximal in the blue wavelength range, roughly around the 4000 Å break.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

First study of reionization in the Planck 2015 normalized closed ΛCDM inflation model

We study reionization in two non-flat ΛCDM inflation models that best fit the Planck 2015 cosmic microwave background (CMB) anisotropy observations, ignoring or in conjunction with baryon acoustic oscillation distance measurements. We implement a principal component analysis (PCA) to estimate the uncertainties in the reionization history from a joint quasar–CMB data set. A thorough Markov Chain Monte Carlo analysis is done over the parameter space of the PCA modes for both non-flat ΛCDM inflation models as well as the original Planck 2016 tilted, spatially flat ΛCDM inflation model. Although both flat and non-flat models can closely match the low-redshift ( z ≲ 6) observations, we notice a possible tension between high-redshift ( z ~ 8) Lyman α emitter data and the non-flat models. This is solely due to the fact that the closed models have a relatively higher reionization optical depth compared to the flat one, which in turn demands more high-redshift ionizing sources and favours an extended reionization starting as early as z ≈ 14. We conclude that as opposed to flat cosmology, for the non-flat cosmology models (i) the escape fraction needs steep redshift evolution and even unrealistically high values at some redshifts and (ii) most of the physical parameters require to have non-monotonic redshift evolution, especially apparent when Lyman α emitter data are included in the analysis.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Factors correlating with significant differences between X-ray structures of myoglobin

Conformational differences between myoglobin structures are studied. Most structural differences in whale myoglobin beyond the uncertainty threshold can be correlated with a few specific structural factors. There are always exceptions and a search for additional factors is needed. The results might have serious implications for biological insights from conformational differences. Validation of general ideas about the origins of conformational differences in proteins is critical in order to arrive at meaningful functional insights. Here, principal component analysis (PCA) and distance difference matrices are used to validate some such ideas about the conformational differences between 291 myoglobin structures from sperm whale, horse and pig. Almost all of the horse and pig structures form compact PCA clusters with only minor coordinate differences and outliers that are easily explained. The 222 whale structures form a few dense clusters with multiple outliers. A few whale outliers with a prominent distortion of the GH loop are very similar to the cluster of horse structures, which all have a similar GH-loop distortion apparently owing to intermolecular crystal lattice hydrogen bonds to the GH loop from residues near the distal histidine His64. The variations of the GH-loop coordinates in the whale structures are likely to be owing to the observed alternative intermolecular crystal lattice bond, with the change to the GH loop distorting bonds correlated with the binding of specific ‘unusual’ ligands. Such an alternative intermolecular bond is not observed in horse myoglobins, obliterating any correlation with the ligands. Intermolecular bonds do not usually cause significant coordinate differences and cannot be validated as their universal cause. Most of the native-like whale myoglobin structure outliers can be correlated with a few specific factors. However, these factors do not always lead to coordinate differences beyond the previously determined uncertainty thresholds. The binding of unusual ligands by myoglobin, leading to crystal-induced distortions, suggests that some of the conformational differences between the apo and holo structures might not be ‘functionally important’ but rather artifacts caused by the binding of ‘unusual’ substrate analogs. The causes of P6 symmetry in myoglobin crystals and the relationship between crystal and solution structures are also discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Application of Principal Component Analysis for the Monitoring of the Aging Process of Nuclear Electrical Cable Insulation

To ensure the safe operation of nuclear power plants (NPPs), it is critical to understand how NPP electrical cable insulation will degrade under different service environments. In this study, various nondestructive examination methods were selected to evaluate degradation of electrical cable insulation after aging. Elongation at break, indenter modulus, relaxation constant, mass change, total color difference, and carbonyl index were collected stepwise on cross-linked polyethylene (XLPE) cable insulations after predetermined exposure intervals. Three different insulation aging scenarios were investigated: 1) simultaneous irradiation and heating at 150°C, 2) heating at 150°C followed by corresponding times of irradiation at ambient temperature, and 3) irradiation at ambient temperature followed by heating at 150°C for the same duration. A constant dose rate of 300 Gy/hr was selected with a total gamma irradiation absorbed dose up to 320 kGy. To gain insight into the long-term performance of NPP electrical cable insulation, principal component analysis (PCA), a data-driven approach, was utilized to identify key indicators of cable insulation degradation. By reducing the dimensionality of the data while retaining degradation information, PCA was used to highlight the changes in the measured properties under gamma irradiation according to total absorbed dose and the different aging scenarios.

Li, Donghui↗

DOE OSTI2017

Automated X-Ray Diffraction of Irradiated Materials

Synchrotron-based X-ray diffraction (XRD) and small-angle Xray scattering (SAXS) characterization techniques used on unirradiated and irradiated reactor pressure vessel steels yield large amounts of data. Machine learning techniques, including PCA, offer a novel method of analyzing and visualizing these large data sets in order to determine the effects of chemistry and irradiation conditions on the formation of radiation induced precipitates. In order to run analysis on these data sets, preprocessing must be carried out to convert the data to a usable format and mask the 2-D detector images to account for experimental variations. Once the data has been preprocessed, it can be organized and visualized using principal component analysis (PCA), multi-dimensional scaling, and k-means clustering. In conclusion, from these techniques, it is shown that sample chemistry has a notable effect on the formation of the radiation induced precipitates in reactor pressure vessel steels.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

A Sparse and Low Rank Penalized Signal Decomposition Model with Constraints: Anomaly Detection in PV Systems

Recently, robust PCA has seen its wide application in various industries for its ability to perform the task of anomaly detection. The essence of robust PCA approach is to break down the signal into a low rank component and sparse component. In many applications, a simple breakdown of the signal without accounting for the signs of low rank components and sparse components would violate the physical constraints of the decomposed signal. In addition, often times, the signals in the real world collected for a long duration has smooth changes within a day and between days. As an example, the power signals collected in a photovoltaic (PV) system are cyclostationary, exhibiting these characteristics. Neglecting the smoothness of signals would result in miss detection of anomalous signals which are smooth within a day but non-smooth between days and vice versa. In this paper, we developed a signal decomposition approach for the purpose of anomaly detection based on the idea of low rank and sparse decomposition taking into consideration the signs of the decomposed low rank and sparse components and the within-day and between-day smooth changes in the original signals. The proposed unsupervised approach for fault detection eliminates the need for faulty samples required by other machine learning methods. It does not require the full I-V characteristics to work. Furthermore, there is no need for complex modelling of PV systems as in the case of power loss analysis. Using Monte Carlo simulations, we demonstrate the ability of our proposed approach for detecting anomalies of different duration and severity in PV systems.

14 SOLAR ENERGY↗

DOE OSTI2015

Neutron spectrometry for UF 6 enrichment verification in storage cylinders

Verification of declared UF 6 enrichment and mass in storage cylinders is of great interest in nuclear material nonproliferation. Nondestructive assay (NDA) techniques are commonly used for safeguards inspections to ensure accountancy of declared nuclear materials. Common NDA techniques used include gamma-ray spectrometry and both passive and active neutron measurements. In the present study, neutron spectrometry was investigated for verification of UF 6 enrichment in 30B storage cylinders based on an unattended and passive measurement approach. MCNP5 and Geant4 simulated neutron spectra, for selected UF 6 enrichments and filling profiles, were used in the investigation. The simulated neutron spectra were analyzed using principal component analysis (PCA). The PCA technique is a well-established technique and has a wide area of application including feature analysis, outlier detection, and gamma-ray spectral analysis. Results obtained demonstrate that neutron spectrometry supported by spectral feature analysis has potential for assaying UF 6 enrichment in storage cylinders. The results from the present study also showed that difficulties associated with the UF 6 filling profile and observed in other unattended passive neutron measurements can possibly be overcome using the approach presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Engineering 2‐Pyrone‐4,6‐Dicarboxylic Acid Production Reveals Metabolic Plasticity of Poplar

Woody biomass is a promising source of fermentable sugars for biofuels and bio-based chemicals, but its industrial use is limited by the costly biorefinery process. A viable strategy to reduce costs involves enhancing both biomass processability and the generation of high-value co-products. Here, we report the implementation of a synthetic metabolic pathway in Populus tremula × P. alba to produce 2-pyrone-4,6-dicarboxylic acid (PDC), a key building block for biodegradable plastics and high-performance materials. This artificial pathway—comprising microbial genes AroG, QsuB, PmdA, PmdB, and PmdC—enabled de novo PDC production in the stems of transgenic poplar. Pathway expression also induced substantial metabolic reprogramming and altered cell wall composition. These include the hyperaccumulation of simple phenolics like protocatechuic acid (PCA) and vanillic acid (VA), alongside reduced levels of p-hydroxybenzoic acid. A large portion of VA was ester-linked to cell wall lignin, while PCA was incorporated into the lignin backbone, forming novel benzodioxane units; concurrently, lignin in transgenic plants exhibited a drastic reduction in guaiacyl- and syringyl-units, with a notable increase in p-hydroxyphenyl-units. Hemicellulose content, particularly xylan, was also significantly increased. Moreover, expression of the PDC-pathway led to the formation of novel VA-derived suberin aromatics, enhancing suberization in bark and roots and improving salt stress tolerance. These changes led to improved saccharification efficiency, with up to 25% more glucose and 2.5 times xylose released from woody biomass. These results demonstrate the metabolic flexibility of poplar and highlight its potential for engineering cost-effective, stress-resilient bioenergy crops with enhanced biorefinery traits.

2-pyrone-4↗

DOE OSTI2020

Data Analytics Applied to Coal Fired Boilers for Detecting Leaks

Data analytics were used to detect boiler leaks from five different coal-fired boilers including both subcritical and supercritical systems. Discriminant functions were developed that detected leaks up to two weeks prior to forced plant shutdowns for repairs. The leaks were identified to occur at different sections of the boiler for each plant, including waterwalls, economizer and superheater using conventional process measurement data. Leaking conditions were detected with a high degree of confidence (&NestedLessLess; 1% misclassified observations) and were able to distinguish normal operations from those time periods with steam leaks even while operating the power plants in power cycling mode.Multivariable statistical analyses, including Principal Component (PCA), cluster, and Fischer Discriminant Analysis (FDA) were used to characterize the leak occurrence. Normal and operational states with steam leaks were provided in the original process datasets. These datasets were split into two different groups for training and validation purposes. The data were sorted chronologically, and every third observation was assigned to training the Discriminant Function Model (DFM) while the rest were reserved for validation. PCA was used to reduce dimensionality of the original datasets. Canonical and FDA analyses were used to investigate the relationship between process variables. The outcome of the analyses revealed that nearly 35,000 observations were classified correctly; less than 0.05% of total observations were misclassified to be leaking, i.e. both false positives and false negatives.

Indrawan, Natarianto↗

DOE OSTI2012

Organ sample generator for expected treatment dose construction and adaptive inverse planning optimization

Purpose: To create an organ sample generator (OSG) for expected treatment dose construction and adaptive inverse planning optimization. The OSG generates random samples of organs of interest from a distribution obeying the patient specific organ variation probability density function (PDF) during the course of adaptive radiotherapy. Methods: Principle component analysis (PCA) and a time-varying least-squares regression (LSR) method were used on patient specific geometric variations of organs of interest manifested on multiple daily volumetric images obtained during the treatment course. The construction of the OSG includes the determination of eigenvectors of the organ variation using PCA, and the determination of the corresponding coefficients using time-varying LSR. The coefficients can be either random variables or random functions of the elapsed treatment days depending on the characteristics of organ variation as a stationary or a nonstationary random process. The LSR method with time-varying weighting parameters was applied to the precollected daily volumetric images to determine the function form of the coefficients. Eleven h and n cancer patients with 30 daily cone beam CT images each were included in the evaluation of the OSG. The evaluation was performed using a total of 18 organs of interest, including 15 organs at risk and 3 targets. Results: Geometric variations of organs of interest during h and n cancer radiotherapy can be represented using the first 3 {approx} 4 eigenvectors. These eigenvectors were variable during treatment, and need to be updated using new daily images obtained during the treatment course. The OSG generates random samples of organs of interest from the estimated organ variation PDF of the individual. The accuracy of the estimated PDF can be improved recursively using extra daily image feedback during the treatment course. The average deviations in the estimation of the mean and standard deviation of the organ variation PDF for h and n cancer radiotherapy were less than 2 and 1 mm, respectively, for most organs after the second week of treatment. After the first three weeks of treatment, the mean discrepancy of the dose estimation accuracy was within 1% for most of organs, the corresponding standard deviation was within 2.5% for parotids, the brain stem and the cochleae, and within 1% for other organs. Conclusions: A patient specific OSG is feasible and can be used to generate random samples of organs of interest for the expected treatment dose construction and adaptive inverse planning. The accuracy of the OSG can be improved continuously and recursively during the adaptive treatment course using daily volumetric image feedback.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting

Purpose: Performing lobe-based quantitative analysis of the lung in computed tomography (CT) scans can assist in efforts to better characterize complex diseases such as chronic obstructive pulmonary disease (COPD). While airways and vessels can help to indicate the location of lobe boundaries, segmentations of these structures are not always available, so methods to define the lobes in the absence of these structures are desirable. Methods: The authors present a fully automatic lung lobe segmentation algorithm that is effective in volumetric inspiratory and expiratory computed tomography (CT) datasets. The authors rely on ridge surface image features indicating fissure locations and a novel approach to modeling shape variation in the surfaces defining the lobe boundaries. The authors employ a particle system that efficiently samples ridge surfaces in the image domain and provides a set of candidate fissure locations based on the Hessian matrix. Following this, lobe boundary shape models generated from principal component analysis (PCA) are fit to the particles data to discriminate between fissure and nonfissure candidates. The resulting set of particle points are used to fit thin plate spline (TPS) interpolating surfaces to form the final boundaries between the lung lobes. Results: The authors tested algorithm performance on 50 inspiratory and 50 expiratory CT scans taken from the COPDGene study. Results indicate that the authors' algorithm performs comparably to pulmonologist-generated lung lobe segmentations and can produce good results in cases with accessory fissures, incomplete fissures, advanced emphysema, and low dose acquisition protocols. Dice scores indicate that only 29 out of 500 (5.85%) lobes showed Dice scores lower than 0.9. Two different approaches for evaluating lobe boundary surface discrepancies were applied and indicate that algorithm boundary identification is most accurate in the vicinity of fissures detectable on CT. Conclusions: The proposed algorithm is effective for lung lobe segmentation in absence of auxiliary structures such as vessels and airways. The most challenging cases are those with mostly incomplete, absent, or near-absent fissures and in cases with poorly revealed fissures due to high image noise. However, the authors observe good performance even in the majority of these cases.

62 RADIOLOGY AND NUCLEAR MEDICINE↗